For each frame, we use RangeNet++ [1]} to predict a semantic label for each point and generate a semantic map \(\mathcal {S}_D\) .
RangeNet++ semantically segments a range image generated by a spherical projection of each laser scan.
Briefly, the network is based on the SqueezeSeg architecture proposed by Wu et al. [2]} and uses a DarkNet53 backbone proposed by Redmon et al.  [3]} to improve results by using more parameters, while keeping the approach real-time capable. For more details about the semantic segmentation approach, we refer to the paper of Milioto et al.  [1]}.
The availability of point-wise labels in the field of view of the sensor makes it also possible to integrate the semantic information into the map.
To this end, we add for each surfel the inferred semantic label \(y\)  and the corresponding probability of that label from the semantic segmentation.
